Overview

Dataset statistics

Number of variables33
Number of observations899702
Missing cells8289601
Missing cells (%)27.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory226.5 MiB
Average record size in memory264.0 B

Variable types

Text8
Numeric18
Boolean1
Categorical5
DateTime1

Alerts

explicit is highly imbalanced (56.6%)Imbalance
time_signature is highly imbalanced (68.6%)Imbalance
streams has 893832 (99.3%) missing valuesMissing
track_artists has 799759 (88.9%) missing valuesMissing
chart has 892662 (99.2%) missing valuesMissing
added_at has 505053 (56.1%) missing valuesMissing
track_album_album has 799705 (88.9%) missing valuesMissing
duration_ms has 892662 (99.2%) missing valuesMissing
track_track_number has 799705 (88.9%) missing valuesMissing
rank has 892662 (99.2%) missing valuesMissing
region has 892662 (99.2%) missing valuesMissing
trend has 892662 (99.2%) missing valuesMissing
track_id has unique valuesUnique
artist_popularity has 21527 (2.4%) zerosZeros
key has 107691 (12.0%) zerosZeros
popularity has 228879 (25.4%) zerosZeros
instrumentalness has 202479 (22.5%) zerosZeros

Reproduction

Analysis started2024-06-20 08:08:04.637859
Analysis finished2024-06-20 08:09:48.595692
Duration1 minute and 43.96 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

track_id
Text

UNIQUE 

Distinct899702
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
2024-06-20T11:09:49.117941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters19793444
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique899702 ?
Unique (%)100.0%

Sample

1st row07vS8obfeZbr8H4MgQfXR7
2nd row1PEqh7awkpuepLBSq8ZwqD
3rd row7E8pPgBY84oDaXRcqODavR
4th row0Atml4huw4Fgyk6YSHiK4M
5th row4WYDmIZrwxBHdBYdvi5oQO
ValueCountFrequency (%)
07vs8obfezbr8h4mgqfxr7 1
 
< 0.1%
3fupp0q5e2jkopeuhxiwdw 1
 
< 0.1%
2nbhp7bduwn8ipwqgk2bb6 1
 
< 0.1%
79pora38defv4nfqwy7ld2 1
 
< 0.1%
2ywnfii5tdikapbzrdsssz 1
 
< 0.1%
6tvvi5gdxqwpda07ns0bxi 1
 
< 0.1%
7e8ppgby84odaxrcqodavr 1
 
< 0.1%
0atml4huw4fgyk6yshik4m 1
 
< 0.1%
4wydmizrwxbhdbydvi5oqo 1
 
< 0.1%
0awg4a7t5urmzz4pzvnav3 1
 
< 0.1%
Other values (899692) 899692
> 99.9%
2024-06-20T11:09:49.775810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 421512
 
2.1%
6 421032
 
2.1%
3 421031
 
2.1%
2 420831
 
2.1%
4 420663
 
2.1%
1 419645
 
2.1%
5 419225
 
2.1%
7 397113
 
2.0%
c 306254
 
1.5%
y 305971
 
1.5%
Other values (52) 15840167
80.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19793444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 421512
 
2.1%
6 421032
 
2.1%
3 421031
 
2.1%
2 420831
 
2.1%
4 420663
 
2.1%
1 419645
 
2.1%
5 419225
 
2.1%
7 397113
 
2.0%
c 306254
 
1.5%
y 305971
 
1.5%
Other values (52) 15840167
80.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19793444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 421512
 
2.1%
6 421032
 
2.1%
3 421031
 
2.1%
2 420831
 
2.1%
4 420663
 
2.1%
1 419645
 
2.1%
5 419225
 
2.1%
7 397113
 
2.0%
c 306254
 
1.5%
y 305971
 
1.5%
Other values (52) 15840167
80.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19793444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 421512
 
2.1%
6 421032
 
2.1%
3 421031
 
2.1%
2 420831
 
2.1%
4 420663
 
2.1%
1 419645
 
2.1%
5 419225
 
2.1%
7 397113
 
2.0%
c 306254
 
1.5%
y 305971
 
1.5%
Other values (52) 15840167
80.0%

streams
Real number (ℝ)

MISSING 

Distinct5403
Distinct (%)92.0%
Missing893832
Missing (%)99.3%
Infinite0
Infinite (%)0.0%
Mean34653.939
Minimum1001
Maximum1367372
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-06-20T11:09:49.909613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1512.9
Q14640.25
median17449.5
Q339327.5
95-th percentile136834.85
Maximum1367372
Range1366371
Interquartile range (IQR)34687.25

Descriptive statistics

Standard deviation56904.532
Coefficient of variation (CV)1.6420798
Kurtosis73.326089
Mean34653.939
Median Absolute Deviation (MAD)14170.5
Skewness5.8350789
Sum2.0341862 × 108
Variance3.2381258 × 109
MonotonicityNot monotonic
2024-06-20T11:09:50.046680image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1072 4
 
< 0.1%
3091 4
 
< 0.1%
3999 4
 
< 0.1%
1043 4
 
< 0.1%
1684 4
 
< 0.1%
1739 3
 
< 0.1%
1262 3
 
< 0.1%
1258 3
 
< 0.1%
2270 3
 
< 0.1%
3533 3
 
< 0.1%
Other values (5393) 5835
 
0.6%
(Missing) 893832
99.3%
ValueCountFrequency (%)
1001 1
< 0.1%
1003 2
< 0.1%
1004 1
< 0.1%
1005 1
< 0.1%
1006 1
< 0.1%
1007 1
< 0.1%
1008 2
< 0.1%
1009 1
< 0.1%
1010 1
< 0.1%
1011 1
< 0.1%
ValueCountFrequency (%)
1367372 1
< 0.1%
712180 1
< 0.1%
655611 1
< 0.1%
637771 1
< 0.1%
612271 1
< 0.1%
584624 1
< 0.1%
544626 1
< 0.1%
536140 1
< 0.1%
516066 1
< 0.1%
480622 1
< 0.1%

artist_followers
Real number (ℝ)

Distinct54753
Distinct (%)6.1%
Missing7206
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean1961705
Minimum0
Maximum1.1375993 × 108
Zeros755
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-06-20T11:09:50.179759image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile189
Q15629
median70503
Q3739311
95-th percentile8297882
Maximum1.1375993 × 108
Range1.1375993 × 108
Interquartile range (IQR)733682

Descriptive statistics

Standard deviation7912156.5
Coefficient of variation (CV)4.033306
Kurtosis92.471995
Mean1961705
Median Absolute Deviation (MAD)70135
Skewness8.6964872
Sum1.7508139 × 1012
Variance6.2602221 × 1013
MonotonicityNot monotonic
2024-06-20T11:09:50.309880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4284731 3867
 
0.4%
5328848 2923
 
0.3%
5201016 1907
 
0.2%
2448813 1449
 
0.2%
1320080 1299
 
0.1%
3236824 1238
 
0.1%
25456 1199
 
0.1%
56348 1139
 
0.1%
901251 1069
 
0.1%
21882 991
 
0.1%
Other values (54743) 875415
97.3%
(Missing) 7206
 
0.8%
ValueCountFrequency (%)
0 755
0.1%
1 406
< 0.1%
2 457
0.1%
3 879
0.1%
4 367
< 0.1%
5 333
 
< 0.1%
6 369
< 0.1%
7 383
< 0.1%
8 335
 
< 0.1%
9 318
 
< 0.1%
ValueCountFrequency (%)
113759927 322
 
< 0.1%
108717184 901
0.1%
107854251 81
 
< 0.1%
95921722 356
 
< 0.1%
93515140 111
 
< 0.1%
86988887 615
0.1%
83371408 547
0.1%
82467148 393
< 0.1%
80598088 143
 
< 0.1%
74608422 314
 
< 0.1%

genres
Text

Distinct29232
Distinct (%)3.3%
Missing7186
Missing (%)0.8%
Memory size6.9 MiB
2024-06-20T11:09:50.555022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length254
Median length197
Mean length35.237234
Min length2

Characters and Unicode

Total characters31449795
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5803 ?
Unique (%)0.7%

Sample

1st row['indie pop', 'la indie', 'pov: indie']
2nd row['lilith', 'new wave pop']
3rd row['deep groove house', 'house', 'tech house']
4th row[]
5th row['chill lounge', 'deep chill']
ValueCountFrequency (%)
rock 228639
 
6.0%
pop 202191
 
5.3%
171179
 
4.5%
metal 75415
 
2.0%
hop 73174
 
1.9%
hip 72728
 
1.9%
jazz 68156
 
1.8%
classical 65612
 
1.7%
rap 64905
 
1.7%
indie 64162
 
1.7%
Other values (2947) 2734743
71.6%
2024-06-20T11:09:51.122381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 3965906
 
12.6%
2928388
 
9.3%
a 2066925
 
6.6%
o 1932622
 
6.1%
e 1821837
 
5.8%
r 1683462
 
5.4%
i 1477199
 
4.7%
n 1432633
 
4.6%
c 1297036
 
4.1%
, 1263305
 
4.0%
Other values (34) 11580482
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31449795
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 3965906
 
12.6%
2928388
 
9.3%
a 2066925
 
6.6%
o 1932622
 
6.1%
e 1821837
 
5.8%
r 1683462
 
5.4%
i 1477199
 
4.7%
n 1432633
 
4.6%
c 1297036
 
4.1%
, 1263305
 
4.0%
Other values (34) 11580482
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31449795
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 3965906
 
12.6%
2928388
 
9.3%
a 2066925
 
6.6%
o 1932622
 
6.1%
e 1821837
 
5.8%
r 1683462
 
5.4%
i 1477199
 
4.7%
n 1432633
 
4.6%
c 1297036
 
4.1%
, 1263305
 
4.0%
Other values (34) 11580482
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31449795
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 3965906
 
12.6%
2928388
 
9.3%
a 2066925
 
6.6%
o 1932622
 
6.1%
e 1821837
 
5.8%
r 1683462
 
5.4%
i 1477199
 
4.7%
n 1432633
 
4.6%
c 1297036
 
4.1%
, 1263305
 
4.0%
Other values (34) 11580482
36.8%

album_total_tracks
Real number (ℝ)

Distinct315
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean13.978811
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-06-20T11:09:51.262304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median11
Q316
95-th percentile40
Maximum1000
Range999
Interquartile range (IQR)14

Descriptive statistics

Standard deviation23.814172
Coefficient of variation (CV)1.7035907
Kurtosis539.16167
Mean13.978811
Median Absolute Deviation (MAD)7
Skewness16.926354
Sum12576750
Variance567.11481
MonotonicityNot monotonic
2024-06-20T11:09:51.398780image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 196360
21.8%
12 66779
 
7.4%
10 60596
 
6.7%
11 49021
 
5.4%
13 41237
 
4.6%
14 40468
 
4.5%
15 36478
 
4.1%
2 29902
 
3.3%
16 29740
 
3.3%
9 22690
 
2.5%
Other values (305) 326430
36.3%
ValueCountFrequency (%)
1 196360
21.8%
2 29902
 
3.3%
3 17094
 
1.9%
4 21921
 
2.4%
5 19436
 
2.2%
6 18266
 
2.0%
7 17160
 
1.9%
8 21686
 
2.4%
9 22690
 
2.5%
10 60596
 
6.7%
ValueCountFrequency (%)
1000 1
 
< 0.1%
930 200
< 0.1%
772 2
 
< 0.1%
579 3
 
< 0.1%
578 2
 
< 0.1%
561 3
 
< 0.1%
549 3
 
< 0.1%
548 1
 
< 0.1%
545 4
 
< 0.1%
531 2
 
< 0.1%

track_artists
Text

MISSING 

Distinct45573
Distinct (%)45.6%
Missing799759
Missing (%)88.9%
Memory size6.9 MiB
2024-06-20T11:09:51.632157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length140
Median length74
Mean length11.992366
Min length1

Characters and Unicode

Total characters1198553
Distinct characters1081
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30543 ?
Unique (%)30.6%

Sample

1st rowPhoebe Bridgers
2nd rowOliver Cheatham
3rd rowWillie Nelson
4th rowFlorence + The Machine
5th rowLudwig Ahgren
ValueCountFrequency (%)
the 5725
 
2.9%
2078
 
1.0%
of 789
 
0.4%
john 757
 
0.4%
de 715
 
0.4%
music 686
 
0.3%
and 613
 
0.3%
band 600
 
0.3%
orchestra 527
 
0.3%
johann 512
 
0.3%
Other values (40666) 185113
93.4%
2024-06-20T11:09:52.011032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 103050
 
8.6%
a 98392
 
8.2%
98177
 
8.2%
i 71735
 
6.0%
n 70685
 
5.9%
o 68855
 
5.7%
r 66510
 
5.5%
l 51153
 
4.3%
s 48369
 
4.0%
t 43104
 
3.6%
Other values (1071) 478523
39.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1198553
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 103050
 
8.6%
a 98392
 
8.2%
98177
 
8.2%
i 71735
 
6.0%
n 70685
 
5.9%
o 68855
 
5.7%
r 66510
 
5.5%
l 51153
 
4.3%
s 48369
 
4.0%
t 43104
 
3.6%
Other values (1071) 478523
39.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1198553
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 103050
 
8.6%
a 98392
 
8.2%
98177
 
8.2%
i 71735
 
6.0%
n 70685
 
5.9%
o 68855
 
5.7%
r 66510
 
5.5%
l 51153
 
4.3%
s 48369
 
4.0%
t 43104
 
3.6%
Other values (1071) 478523
39.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1198553
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 103050
 
8.6%
a 98392
 
8.2%
98177
 
8.2%
i 71735
 
6.0%
n 70685
 
5.9%
o 68855
 
5.7%
r 66510
 
5.5%
l 51153
 
4.3%
s 48369
 
4.0%
t 43104
 
3.6%
Other values (1071) 478523
39.9%

artist_popularity
Real number (ℝ)

ZEROS 

Distinct93
Distinct (%)< 0.1%
Missing7186
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean42.487238
Minimum0
Maximum100
Zeros21527
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-06-20T11:09:52.147479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q128
median44
Q358
95-th percentile75
Maximum100
Range100
Interquartile range (IQR)30

Descriptive statistics

Standard deviation21.031606
Coefficient of variation (CV)0.49500995
Kurtosis-0.63972755
Mean42.487238
Median Absolute Deviation (MAD)15
Skewness-0.17761189
Sum37920540
Variance442.32843
MonotonicityNot monotonic
2024-06-20T11:09:52.277609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21527
 
2.4%
51 18810
 
2.1%
52 17200
 
1.9%
58 17061
 
1.9%
45 16698
 
1.9%
38 16050
 
1.8%
44 15785
 
1.8%
42 15774
 
1.8%
39 15735
 
1.7%
46 15683
 
1.7%
Other values (83) 722193
80.3%
ValueCountFrequency (%)
0 21527
2.4%
1 8596
 
1.0%
2 7171
 
0.8%
3 6177
 
0.7%
4 5752
 
0.6%
5 5722
 
0.6%
6 5908
 
0.7%
7 5332
 
0.6%
8 5247
 
0.6%
9 5226
 
0.6%
ValueCountFrequency (%)
100 901
 
0.1%
93 615
 
0.1%
91 811
 
0.1%
90 1043
0.1%
88 483
 
0.1%
87 1872
0.2%
86 961
 
0.1%
85 1391
0.2%
84 2309
0.3%
83 2551
0.3%

explicit
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size6.9 MiB
False
819486 
True
 
80215
(Missing)
 
1
ValueCountFrequency (%)
False 819486
91.1%
True 80215
 
8.9%
(Missing) 1
 
< 0.1%
2024-06-20T11:09:52.392420image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

tempo
Real number (ℝ)

Distinct130141
Distinct (%)14.5%
Missing478
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean119.21053
Minimum0
Maximum249.899
Zeros529
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-06-20T11:09:52.504979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile74.197
Q195.374
median119.984
Q3138.451
95-th percentile174.037
Maximum249.899
Range249.899
Interquartile range (IQR)43.077

Descriptive statistics

Standard deviation30.494827
Coefficient of variation (CV)0.25580649
Kurtosis-0.25019337
Mean119.21053
Median Absolute Deviation (MAD)21.048
Skewness0.28667769
Sum1.0719697 × 108
Variance929.9345
MonotonicityNot monotonic
2024-06-20T11:09:52.639643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 529
 
0.1%
119.999 308
 
< 0.1%
120.007 277
 
< 0.1%
119.994 271
 
< 0.1%
120.008 264
 
< 0.1%
120.004 263
 
< 0.1%
120.012 263
 
< 0.1%
120.006 263
 
< 0.1%
120.005 262
 
< 0.1%
120.003 260
 
< 0.1%
Other values (130131) 896264
99.6%
(Missing) 478
 
0.1%
ValueCountFrequency (%)
0 529
0.1%
30.038 1
 
< 0.1%
30.046 1
 
< 0.1%
30.121 1
 
< 0.1%
30.133 1
 
< 0.1%
30.31 1
 
< 0.1%
30.485 1
 
< 0.1%
30.51 1
 
< 0.1%
30.559 1
 
< 0.1%
30.846 1
 
< 0.1%
ValueCountFrequency (%)
249.899 1
< 0.1%
248.054 1
< 0.1%
244.44 1
< 0.1%
244.08 1
< 0.1%
243.372 1
< 0.1%
243.193 1
< 0.1%
242.998 1
< 0.1%
241.647 1
< 0.1%
241.426 1
< 0.1%
240.947 1
< 0.1%

chart
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing892662
Missing (%)99.2%
Memory size6.9 MiB
top200
5870 
viral50
1170 

Length

Max length7
Median length6
Mean length6.1661932
Min length6

Characters and Unicode

Total characters43410
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtop200
2nd rowtop200
3rd rowtop200
4th rowtop200
5th rowtop200

Common Values

ValueCountFrequency (%)
top200 5870
 
0.7%
viral50 1170
 
0.1%
(Missing) 892662
99.2%

Length

2024-06-20T11:09:52.764617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T11:09:52.858060image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
top200 5870
83.4%
viral50 1170
 
16.6%

Most occurring characters

ValueCountFrequency (%)
0 12910
29.7%
t 5870
13.5%
o 5870
13.5%
p 5870
13.5%
2 5870
13.5%
v 1170
 
2.7%
i 1170
 
2.7%
r 1170
 
2.7%
a 1170
 
2.7%
l 1170
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12910
29.7%
t 5870
13.5%
o 5870
13.5%
p 5870
13.5%
2 5870
13.5%
v 1170
 
2.7%
i 1170
 
2.7%
r 1170
 
2.7%
a 1170
 
2.7%
l 1170
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12910
29.7%
t 5870
13.5%
o 5870
13.5%
p 5870
13.5%
2 5870
13.5%
v 1170
 
2.7%
i 1170
 
2.7%
r 1170
 
2.7%
a 1170
 
2.7%
l 1170
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12910
29.7%
t 5870
13.5%
o 5870
13.5%
p 5870
13.5%
2 5870
13.5%
v 1170
 
2.7%
i 1170
 
2.7%
r 1170
 
2.7%
a 1170
 
2.7%
l 1170
 
2.7%
Distinct16248
Distinct (%)1.8%
Missing1
Missing (%)< 0.1%
Memory size6.9 MiB
2024-06-20T11:09:53.068151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.6155156
Min length4

Characters and Unicode

Total characters8651089
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2329 ?
Unique (%)0.3%

Sample

1st row2018-12-05
2nd row1996-04-16
3rd row2014-07-07
4th row2001-01-24
5th row2014-10-03
ValueCountFrequency (%)
2013-01-01 4695
 
0.5%
2007-01-01 4064
 
0.5%
2006-01-01 3974
 
0.4%
2008-01-01 3940
 
0.4%
2009-01-01 3552
 
0.4%
2010-01-01 3546
 
0.4%
2012-01-01 3511
 
0.4%
2011-01-01 3499
 
0.4%
2005-01-01 3402
 
0.4%
2014-01-01 3015
 
0.3%
Other values (16238) 862503
95.9%
2024-06-20T11:09:53.419032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2120765
24.5%
- 1684095
19.5%
2 1635881
18.9%
1 1418970
16.4%
9 405951
 
4.7%
3 308568
 
3.6%
8 233204
 
2.7%
7 222587
 
2.6%
6 212451
 
2.5%
4 207017
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8651089
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2120765
24.5%
- 1684095
19.5%
2 1635881
18.9%
1 1418970
16.4%
9 405951
 
4.7%
3 308568
 
3.6%
8 233204
 
2.7%
7 222587
 
2.6%
6 212451
 
2.5%
4 207017
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8651089
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2120765
24.5%
- 1684095
19.5%
2 1635881
18.9%
1 1418970
16.4%
9 405951
 
4.7%
3 308568
 
3.6%
8 233204
 
2.7%
7 222587
 
2.6%
6 212451
 
2.5%
4 207017
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8651089
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2120765
24.5%
- 1684095
19.5%
2 1635881
18.9%
1 1418970
16.4%
9 405951
 
4.7%
3 308568
 
3.6%
8 233204
 
2.7%
7 222587
 
2.6%
6 212451
 
2.5%
4 207017
 
2.4%

energy
Real number (ℝ)

Distinct3138
Distinct (%)0.3%
Missing478
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.53541322
Minimum0
Maximum1
Zeros31
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-06-20T11:09:53.557668image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0434
Q10.296
median0.567
Q30.783
95-th percentile0.949
Maximum1
Range1
Interquartile range (IQR)0.487

Descriptive statistics

Standard deviation0.28827418
Coefficient of variation (CV)0.53841439
Kurtosis-1.1233676
Mean0.53541322
Median Absolute Deviation (MAD)0.239
Skewness-0.23737546
Sum481456.42
Variance0.083102005
MonotonicityNot monotonic
2024-06-20T11:09:53.689988image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.931 1251
 
0.1%
0.676 1225
 
0.1%
0.868 1211
 
0.1%
0.725 1202
 
0.1%
0.721 1195
 
0.1%
0.727 1195
 
0.1%
0.719 1195
 
0.1%
0.724 1195
 
0.1%
0.829 1194
 
0.1%
0.871 1192
 
0.1%
Other values (3128) 887169
98.6%
ValueCountFrequency (%)
0 31
< 0.1%
1.7 × 10-51
 
< 0.1%
1.74 × 10-51
 
< 0.1%
1.93 × 10-51
 
< 0.1%
1.94 × 10-51
 
< 0.1%
1.98 × 10-51
 
< 0.1%
2 × 10-53
 
< 0.1%
2.01 × 10-51
 
< 0.1%
2.02 × 10-510
 
< 0.1%
2.03 × 10-520
< 0.1%
ValueCountFrequency (%)
1 345
< 0.1%
0.999 565
0.1%
0.998 527
0.1%
0.997 592
0.1%
0.996 651
0.1%
0.995 785
0.1%
0.994 751
0.1%
0.993 829
0.1%
0.992 710
0.1%
0.991 842
0.1%

key
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)< 0.1%
Missing478
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean5.2258425
Minimum0
Maximum11
Zeros107691
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-06-20T11:09:53.797866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5559433
Coefficient of variation (CV)0.68045358
Kurtosis-1.2870265
Mean5.2258425
Median Absolute Deviation (MAD)3
Skewness0.016818853
Sum4699203
Variance12.644732
MonotonicityNot monotonic
2024-06-20T11:09:53.897062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 107691
12.0%
7 103740
11.5%
2 95624
10.6%
9 88393
9.8%
1 83447
9.3%
5 79262
8.8%
4 66637
7.4%
11 66151
7.4%
10 61828
6.9%
8 57407
6.4%
Other values (2) 89044
9.9%
ValueCountFrequency (%)
0 107691
12.0%
1 83447
9.3%
2 95624
10.6%
3 33176
 
3.7%
4 66637
7.4%
5 79262
8.8%
6 55868
6.2%
7 103740
11.5%
8 57407
6.4%
9 88393
9.8%
ValueCountFrequency (%)
11 66151
7.4%
10 61828
6.9%
9 88393
9.8%
8 57407
6.4%
7 103740
11.5%
6 55868
6.2%
5 79262
8.8%
4 66637
7.4%
3 33176
 
3.7%
2 95624
10.6%

added_at
Date

MISSING 

Distinct259021
Distinct (%)65.6%
Missing505053
Missing (%)56.1%
Memory size6.9 MiB
Minimum1970-01-01 00:00:00+00:00
Maximum2024-03-09 23:31:45+00:00
2024-06-20T11:09:54.014772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:54.156681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

popularity
Real number (ℝ)

ZEROS 

Distinct99
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean21.881982
Minimum0
Maximum98
Zeros228879
Zeros (%)25.4%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-06-20T11:09:54.299679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20
Q337
95-th percentile57
Maximum98
Range98
Interquartile range (IQR)37

Descriptive statistics

Standard deviation20.01999
Coefficient of variation (CV)0.91490754
Kurtosis-0.80735345
Mean21.881982
Median Absolute Deviation (MAD)19
Skewness0.51035155
Sum19687241
Variance400.80001
MonotonicityNot monotonic
2024-06-20T11:09:54.434749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 228879
25.4%
1 20977
 
2.3%
2 16035
 
1.8%
3 13568
 
1.5%
31 13455
 
1.5%
32 13353
 
1.5%
29 13340
 
1.5%
30 13290
 
1.5%
33 13251
 
1.5%
28 13108
 
1.5%
Other values (89) 540445
60.1%
ValueCountFrequency (%)
0 228879
25.4%
1 20977
 
2.3%
2 16035
 
1.8%
3 13568
 
1.5%
4 12509
 
1.4%
5 10854
 
1.2%
6 9756
 
1.1%
7 9672
 
1.1%
8 9869
 
1.1%
9 9949
 
1.1%
ValueCountFrequency (%)
98 2
 
< 0.1%
97 1
 
< 0.1%
96 1
 
< 0.1%
95 2
 
< 0.1%
94 4
 
< 0.1%
93 6
 
< 0.1%
92 3
 
< 0.1%
91 14
< 0.1%
90 13
< 0.1%
89 21
< 0.1%

track_album_album
Categorical

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing799705
Missing (%)88.9%
Memory size6.9 MiB
album
57351 
single
32980 
compilation
9666 

Length

Max length11
Median length5
Mean length5.9097873
Min length5

Characters and Unicode

Total characters590961
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsingle
2nd rowcompilation
3rd rowalbum
4th rowalbum
5th rowsingle

Common Values

ValueCountFrequency (%)
album 57351
 
6.4%
single 32980
 
3.7%
compilation 9666
 
1.1%
(Missing) 799705
88.9%

Length

2024-06-20T11:09:54.708294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T11:09:54.798979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
album 57351
57.4%
single 32980
33.0%
compilation 9666
 
9.7%

Most occurring characters

ValueCountFrequency (%)
l 99997
16.9%
a 67017
11.3%
m 67017
11.3%
b 57351
9.7%
u 57351
9.7%
i 52312
8.9%
n 42646
7.2%
s 32980
 
5.6%
g 32980
 
5.6%
e 32980
 
5.6%
Other values (4) 48330
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 590961
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 99997
16.9%
a 67017
11.3%
m 67017
11.3%
b 57351
9.7%
u 57351
9.7%
i 52312
8.9%
n 42646
7.2%
s 32980
 
5.6%
g 32980
 
5.6%
e 32980
 
5.6%
Other values (4) 48330
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 590961
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 99997
16.9%
a 67017
11.3%
m 67017
11.3%
b 57351
9.7%
u 57351
9.7%
i 52312
8.9%
n 42646
7.2%
s 32980
 
5.6%
g 32980
 
5.6%
e 32980
 
5.6%
Other values (4) 48330
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 590961
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 99997
16.9%
a 67017
11.3%
m 67017
11.3%
b 57351
9.7%
u 57351
9.7%
i 52312
8.9%
n 42646
7.2%
s 32980
 
5.6%
g 32980
 
5.6%
e 32980
 
5.6%
Other values (4) 48330
8.2%

duration_ms
Real number (ℝ)

MISSING 

Distinct6202
Distinct (%)88.1%
Missing892662
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean218893.17
Minimum0
Maximum1037586
Zeros19
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-06-20T11:09:54.913729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile151342.1
Q1187948.25
median212133
Q3241894.25
95-th percentile306124.2
Maximum1037586
Range1037586
Interquartile range (IQR)53946

Descriptive statistics

Standard deviation54665.606
Coefficient of variation (CV)0.24973646
Kurtosis17.739926
Mean218893.17
Median Absolute Deviation (MAD)26815
Skewness2.0521907
Sum1.5410079 × 109
Variance2.9883284 × 109
MonotonicityNot monotonic
2024-06-20T11:09:55.054923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
< 0.1%
180000 9
 
< 0.1%
240000 8
 
< 0.1%
193893 6
 
< 0.1%
216000 6
 
< 0.1%
184000 5
 
< 0.1%
228000 5
 
< 0.1%
192000 5
 
< 0.1%
224000 5
 
< 0.1%
200000 4
 
< 0.1%
Other values (6192) 6968
 
0.8%
(Missing) 892662
99.2%
ValueCountFrequency (%)
0 19
< 0.1%
35975 1
 
< 0.1%
46768 1
 
< 0.1%
48422 1
 
< 0.1%
50747 1
 
< 0.1%
60400 1
 
< 0.1%
60453 1
 
< 0.1%
68691 1
 
< 0.1%
74746 1
 
< 0.1%
76500 1
 
< 0.1%
ValueCountFrequency (%)
1037586 1
< 0.1%
913214 1
< 0.1%
755855 1
< 0.1%
721000 1
< 0.1%
602296 1
< 0.1%
589640 1
< 0.1%
589094 1
< 0.1%
579293 1
< 0.1%
577819 1
< 0.1%
566133 1
< 0.1%
Distinct13061
Distinct (%)1.5%
Missing1
Missing (%)< 0.1%
Memory size6.9 MiB
2024-06-20T11:09:55.335138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length1110
Median length1104
Mean length797.92245
Min length2

Characters and Unicode

Total characters717891622
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6665 ?
Unique (%)0.7%

Sample

1st row[]
2nd row['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']
3rd row[]
4th row[]
5th row['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'PR', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']
ValueCountFrequency (%)
cw 659114
 
0.6%
pt 655940
 
0.5%
no 655909
 
0.5%
fi 655739
 
0.5%
nl 655727
 
0.5%
es 655682
 
0.5%
hu 655369
 
0.5%
lt 655112
 
0.5%
ee 655093
 
0.5%
be 654971
 
0.5%
Other values (176) 113231457
94.5%
2024-06-20T11:09:55.774971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 239155698
33.3%
, 118890412
16.6%
118890412
16.6%
M 18079190
 
2.5%
G 14891516
 
2.1%
T 14877707
 
2.1%
S 14212546
 
2.0%
A 13619486
 
1.9%
B 13535602
 
1.9%
L 12313162
 
1.7%
Other values (21) 139425891
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 717891622
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 239155698
33.3%
, 118890412
16.6%
118890412
16.6%
M 18079190
 
2.5%
G 14891516
 
2.1%
T 14877707
 
2.1%
S 14212546
 
2.0%
A 13619486
 
1.9%
B 13535602
 
1.9%
L 12313162
 
1.7%
Other values (21) 139425891
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 717891622
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 239155698
33.3%
, 118890412
16.6%
118890412
16.6%
M 18079190
 
2.5%
G 14891516
 
2.1%
T 14877707
 
2.1%
S 14212546
 
2.0%
A 13619486
 
1.9%
B 13535602
 
1.9%
L 12313162
 
1.7%
Other values (21) 139425891
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 717891622
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 239155698
33.3%
, 118890412
16.6%
118890412
16.6%
M 18079190
 
2.5%
G 14891516
 
2.1%
T 14877707
 
2.1%
S 14212546
 
2.0%
A 13619486
 
1.9%
B 13535602
 
1.9%
L 12313162
 
1.7%
Other values (21) 139425891
19.4%

track_track_number
Real number (ℝ)

MISSING 

Distinct123
Distinct (%)0.1%
Missing799705
Missing (%)88.9%
Infinite0
Infinite (%)0.0%
Mean5.6861006
Minimum1
Maximum481
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-06-20T11:09:55.910982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q38
95-th percentile17
Maximum481
Range480
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.7857578
Coefficient of variation (CV)1.3692614
Kurtosis306.45414
Mean5.6861006
Median Absolute Deviation (MAD)2
Skewness9.072038
Sum568593
Variance60.618025
MonotonicityNot monotonic
2024-06-20T11:09:56.041894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 35065
 
3.9%
2 9603
 
1.1%
3 7581
 
0.8%
4 6385
 
0.7%
5 5569
 
0.6%
6 5009
 
0.6%
7 4335
 
0.5%
8 3993
 
0.4%
9 3608
 
0.4%
10 3190
 
0.4%
Other values (113) 15659
 
1.7%
(Missing) 799705
88.9%
ValueCountFrequency (%)
1 35065
3.9%
2 9603
 
1.1%
3 7581
 
0.8%
4 6385
 
0.7%
5 5569
 
0.6%
6 5009
 
0.6%
7 4335
 
0.5%
8 3993
 
0.4%
9 3608
 
0.4%
10 3190
 
0.4%
ValueCountFrequency (%)
481 1
< 0.1%
461 1
< 0.1%
209 1
< 0.1%
197 1
< 0.1%
188 1
< 0.1%
184 1
< 0.1%
170 1
< 0.1%
150 1
< 0.1%
149 1
< 0.1%
138 1
< 0.1%

rank
Real number (ℝ)

MISSING 

Distinct200
Distinct (%)2.8%
Missing892662
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean111.94972
Minimum1
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-06-20T11:09:56.168470image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q149
median121
Q3171
95-th percentile196
Maximum200
Range199
Interquartile range (IQR)122

Descriptive statistics

Standard deviation62.070924
Coefficient of variation (CV)0.55445361
Kurtosis-1.3810954
Mean111.94972
Median Absolute Deviation (MAD)58
Skewness-0.17751291
Sum788126
Variance3852.7996
MonotonicityNot monotonic
2024-06-20T11:09:56.308933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49 91
 
< 0.1%
195 89
 
< 0.1%
198 85
 
< 0.1%
50 83
 
< 0.1%
47 83
 
< 0.1%
193 78
 
< 0.1%
197 77
 
< 0.1%
200 77
 
< 0.1%
199 75
 
< 0.1%
196 72
 
< 0.1%
Other values (190) 6230
 
0.7%
(Missing) 892662
99.2%
ValueCountFrequency (%)
1 26
< 0.1%
2 26
< 0.1%
3 24
< 0.1%
4 25
< 0.1%
5 26
< 0.1%
6 23
< 0.1%
7 21
< 0.1%
8 27
< 0.1%
9 18
< 0.1%
10 32
< 0.1%
ValueCountFrequency (%)
200 77
< 0.1%
199 75
< 0.1%
198 85
< 0.1%
197 77
< 0.1%
196 72
< 0.1%
195 89
< 0.1%
194 59
< 0.1%
193 78
< 0.1%
192 59
< 0.1%
191 51
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing478
Missing (%)0.1%
Memory size6.9 MiB
1.0
564721 
0.0
334503 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2697672
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 564721
62.8%
0.0 334503
37.2%
(Missing) 478
 
0.1%

Length

2024-06-20T11:09:56.431703image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T11:09:56.526168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 564721
62.8%
0.0 334503
37.2%

Most occurring characters

ValueCountFrequency (%)
0 1233727
45.7%
. 899224
33.3%
1 564721
20.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2697672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1233727
45.7%
. 899224
33.3%
1 564721
20.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2697672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1233727
45.7%
. 899224
33.3%
1 564721
20.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2697672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1233727
45.7%
. 899224
33.3%
1 564721
20.9%

time_signature
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing478
Missing (%)0.1%
Memory size6.9 MiB
4.0
773496 
3.0
95821 
5.0
 
18621
1.0
 
10732
0.0
 
554

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2697672
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 773496
86.0%
3.0 95821
 
10.7%
5.0 18621
 
2.1%
1.0 10732
 
1.2%
0.0 554
 
0.1%
(Missing) 478
 
0.1%

Length

2024-06-20T11:09:56.624703image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T11:09:56.722829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
4.0 773496
86.0%
3.0 95821
 
10.7%
5.0 18621
 
2.1%
1.0 10732
 
1.2%
0.0 554
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 899778
33.4%
. 899224
33.3%
4 773496
28.7%
3 95821
 
3.6%
5 18621
 
0.7%
1 10732
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2697672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 899778
33.4%
. 899224
33.3%
4 773496
28.7%
3 95821
 
3.6%
5 18621
 
0.7%
1 10732
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2697672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 899778
33.4%
. 899224
33.3%
4 773496
28.7%
3 95821
 
3.6%
5 18621
 
0.7%
1 10732
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2697672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 899778
33.4%
. 899224
33.3%
4 773496
28.7%
3 95821
 
3.6%
5 18621
 
0.7%
1 10732
 
0.4%
Distinct375150
Distinct (%)41.7%
Missing431
Missing (%)< 0.1%
Memory size6.9 MiB
2024-06-20T11:09:57.042324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length292
Median length207
Mean length21.304499
Min length1

Characters and Unicode

Total characters19158518
Distinct characters3645
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique254978 ?
Unique (%)28.4%

Sample

1st rowSpotify Singles
2nd rowNow in a Minute
3rd rowLove Too Deep
4th rowVoces Del Pueblo
5th rowThe Smooth Operator - Cosmopolitan Lounge Music
ValueCountFrequency (%)
the 144530
 
4.4%
86369
 
2.6%
of 60265
 
1.8%
a 29419
 
0.9%
vol 28391
 
0.9%
in 25077
 
0.8%
original 24140
 
0.7%
music 23470
 
0.7%
and 22095
 
0.7%
soundtrack 21417
 
0.7%
Other values (138727) 2797248
85.7%
2024-06-20T11:09:57.524795image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2363052
 
12.3%
e 1628552
 
8.5%
o 1124779
 
5.9%
a 1117105
 
5.8%
i 1071258
 
5.6%
n 964116
 
5.0%
r 881772
 
4.6%
t 843748
 
4.4%
s 803558
 
4.2%
l 677949
 
3.5%
Other values (3635) 7682629
40.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19158518
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2363052
 
12.3%
e 1628552
 
8.5%
o 1124779
 
5.9%
a 1117105
 
5.8%
i 1071258
 
5.6%
n 964116
 
5.0%
r 881772
 
4.6%
t 843748
 
4.4%
s 803558
 
4.2%
l 677949
 
3.5%
Other values (3635) 7682629
40.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19158518
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2363052
 
12.3%
e 1628552
 
8.5%
o 1124779
 
5.9%
a 1117105
 
5.8%
i 1071258
 
5.6%
n 964116
 
5.0%
r 881772
 
4.6%
t 843748
 
4.4%
s 803558
 
4.2%
l 677949
 
3.5%
Other values (3635) 7682629
40.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19158518
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2363052
 
12.3%
e 1628552
 
8.5%
o 1124779
 
5.9%
a 1117105
 
5.8%
i 1071258
 
5.6%
n 964116
 
5.0%
r 881772
 
4.6%
t 843748
 
4.4%
s 803558
 
4.2%
l 677949
 
3.5%
Other values (3635) 7682629
40.1%

speechiness
Real number (ℝ)

Distinct1651
Distinct (%)0.2%
Missing478
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.080717233
Minimum0
Maximum0.967
Zeros530
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-06-20T11:09:57.661227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0287
Q10.0361
median0.0465
Q30.0768
95-th percentile0.277
Maximum0.967
Range0.967
Interquartile range (IQR)0.0407

Descriptive statistics

Standard deviation0.093727413
Coefficient of variation (CV)1.1611822
Kurtosis21.711037
Mean0.080717233
Median Absolute Deviation (MAD)0.0135
Skewness3.9205674
Sum72582.873
Variance0.0087848279
MonotonicityNot monotonic
2024-06-20T11:09:57.794753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0365 2760
 
0.3%
0.0342 2724
 
0.3%
0.0337 2707
 
0.3%
0.0348 2694
 
0.3%
0.0341 2682
 
0.3%
0.0346 2679
 
0.3%
0.0347 2666
 
0.3%
0.0353 2664
 
0.3%
0.0367 2652
 
0.3%
0.0366 2649
 
0.3%
Other values (1641) 872347
97.0%
ValueCountFrequency (%)
0 530
0.1%
0.0216 1
 
< 0.1%
0.0217 1
 
< 0.1%
0.0218 3
 
< 0.1%
0.022 6
 
< 0.1%
0.0221 3
 
< 0.1%
0.0222 3
 
< 0.1%
0.0223 12
 
< 0.1%
0.0224 12
 
< 0.1%
0.0225 10
 
< 0.1%
ValueCountFrequency (%)
0.967 3
 
< 0.1%
0.966 1
 
< 0.1%
0.965 2
 
< 0.1%
0.964 2
 
< 0.1%
0.963 7
 
< 0.1%
0.962 9
 
< 0.1%
0.961 14
< 0.1%
0.96 16
< 0.1%
0.959 18
< 0.1%
0.958 30
< 0.1%

region
Text

MISSING 

Distinct66
Distinct (%)0.9%
Missing892662
Missing (%)99.2%
Memory size6.9 MiB
2024-06-20T11:09:57.973933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length18
Median length13
Mean length7.9174716
Min length4

Characters and Unicode

Total characters55739
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFrance
2nd rowSaudi Arabia
3rd rowAustralia
4th rowAustralia
5th rowHonduras
ValueCountFrequency (%)
australia 559
 
7.1%
argentina 450
 
5.7%
united 345
 
4.4%
brazil 338
 
4.3%
austria 309
 
3.9%
canada 304
 
3.8%
ireland 289
 
3.6%
turkey 216
 
2.7%
republic 213
 
2.7%
kingdom 198
 
2.5%
Other values (64) 4698
59.3%
2024-06-20T11:09:58.284519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 7563
13.6%
i 5072
 
9.1%
n 4942
 
8.9%
e 4305
 
7.7%
r 3582
 
6.4%
l 3031
 
5.4%
t 2584
 
4.6%
d 2214
 
4.0%
u 2070
 
3.7%
o 1890
 
3.4%
Other values (36) 18486
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55739
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 7563
13.6%
i 5072
 
9.1%
n 4942
 
8.9%
e 4305
 
7.7%
r 3582
 
6.4%
l 3031
 
5.4%
t 2584
 
4.6%
d 2214
 
4.0%
u 2070
 
3.7%
o 1890
 
3.4%
Other values (36) 18486
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55739
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 7563
13.6%
i 5072
 
9.1%
n 4942
 
8.9%
e 4305
 
7.7%
r 3582
 
6.4%
l 3031
 
5.4%
t 2584
 
4.6%
d 2214
 
4.0%
u 2070
 
3.7%
o 1890
 
3.4%
Other values (36) 18486
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55739
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 7563
13.6%
i 5072
 
9.1%
n 4942
 
8.9%
e 4305
 
7.7%
r 3582
 
6.4%
l 3031
 
5.4%
t 2584
 
4.6%
d 2214
 
4.0%
u 2070
 
3.7%
o 1890
 
3.4%
Other values (36) 18486
33.2%

danceability
Real number (ℝ)

Distinct1354
Distinct (%)0.2%
Missing478
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.55023863
Minimum0
Maximum0.995
Zeros531
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-06-20T11:09:58.420657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.191
Q10.425
median0.57
Q30.694
95-th percentile0.83
Maximum0.995
Range0.995
Interquartile range (IQR)0.269

Descriptive statistics

Standard deviation0.19085016
Coefficient of variation (CV)0.34684979
Kurtosis-0.44239009
Mean0.55023863
Median Absolute Deviation (MAD)0.133
Skewness-0.391061
Sum494787.78
Variance0.036423782
MonotonicityNot monotonic
2024-06-20T11:09:58.558971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.599 1938
 
0.2%
0.676 1921
 
0.2%
0.633 1913
 
0.2%
0.667 1913
 
0.2%
0.622 1909
 
0.2%
0.602 1907
 
0.2%
0.639 1898
 
0.2%
0.637 1893
 
0.2%
0.608 1890
 
0.2%
0.603 1886
 
0.2%
Other values (1344) 880156
97.8%
ValueCountFrequency (%)
0 531
0.1%
0.0237 1
 
< 0.1%
0.0443 1
 
< 0.1%
0.0513 1
 
< 0.1%
0.0528 1
 
< 0.1%
0.0531 1
 
< 0.1%
0.0532 1
 
< 0.1%
0.0535 1
 
< 0.1%
0.0536 1
 
< 0.1%
0.0537 1
 
< 0.1%
ValueCountFrequency (%)
0.995 1
 
< 0.1%
0.994 1
 
< 0.1%
0.991 1
 
< 0.1%
0.99 2
 
< 0.1%
0.989 1
 
< 0.1%
0.988 7
< 0.1%
0.987 9
< 0.1%
0.986 9
< 0.1%
0.985 13
< 0.1%
0.984 11
< 0.1%

valence
Real number (ℝ)

Distinct2085
Distinct (%)0.2%
Missing478
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.43714447
Minimum0
Maximum1
Zeros998
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-06-20T11:09:58.694610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0412
Q10.202
median0.411
Q30.655
95-th percentile0.908
Maximum1
Range1
Interquartile range (IQR)0.453

Descriptive statistics

Standard deviation0.27094097
Coefficient of variation (CV)0.61979731
Kurtosis-1.0574219
Mean0.43714447
Median Absolute Deviation (MAD)0.224
Skewness0.25175086
Sum393090.79
Variance0.073409007
MonotonicityNot monotonic
2024-06-20T11:09:58.968762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961 2162
 
0.2%
0.962 1879
 
0.2%
0.963 1684
 
0.2%
0.964 1591
 
0.2%
0.965 1356
 
0.2%
0.96 1352
 
0.2%
0.158 1327
 
0.1%
0.196 1321
 
0.1%
0.156 1275
 
0.1%
0.193 1272
 
0.1%
Other values (2075) 884005
98.3%
ValueCountFrequency (%)
0 998
0.1%
1 × 10-5849
0.1%
7.58 × 10-51
 
< 0.1%
8.01 × 10-51
 
< 0.1%
8.46 × 10-51
 
< 0.1%
0.000119 1
 
< 0.1%
0.000147 1
 
< 0.1%
0.000153 1
 
< 0.1%
0.000166 1
 
< 0.1%
0.000253 1
 
< 0.1%
ValueCountFrequency (%)
1 29
< 0.1%
0.999 8
 
< 0.1%
0.998 8
 
< 0.1%
0.997 5
 
< 0.1%
0.996 10
 
< 0.1%
0.995 9
 
< 0.1%
0.994 16
< 0.1%
0.993 17
< 0.1%
0.992 24
< 0.1%
0.991 20
< 0.1%

acousticness
Real number (ℝ)

Distinct5391
Distinct (%)0.6%
Missing478
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.41457313
Minimum0
Maximum0.996
Zeros127
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-06-20T11:09:59.102217image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.000487
Q10.0382
median0.308
Q30.814
95-th percentile0.988
Maximum0.996
Range0.996
Interquartile range (IQR)0.7758

Descriptive statistics

Standard deviation0.37564326
Coefficient of variation (CV)0.90609649
Kurtosis-1.520845
Mean0.41457313
Median Absolute Deviation (MAD)0.30091
Skewness0.32632981
Sum372794.11
Variance0.14110786
MonotonicityNot monotonic
2024-06-20T11:09:59.239764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.995 8174
 
0.9%
0.994 7038
 
0.8%
0.993 6163
 
0.7%
0.992 5277
 
0.6%
0.991 4884
 
0.5%
0.99 4144
 
0.5%
0.989 3810
 
0.4%
0.988 3573
 
0.4%
0.996 3308
 
0.4%
0.987 3272
 
0.4%
Other values (5381) 849581
94.4%
ValueCountFrequency (%)
0 127
< 0.1%
1 × 10-63
 
< 0.1%
1.01 × 10-67
 
< 0.1%
1.02 × 10-65
 
< 0.1%
1.03 × 10-66
 
< 0.1%
1.04 × 10-64
 
< 0.1%
1.05 × 10-63
 
< 0.1%
1.06 × 10-62
 
< 0.1%
1.07 × 10-62
 
< 0.1%
1.08 × 10-62
 
< 0.1%
ValueCountFrequency (%)
0.996 3308
0.4%
0.995 8174
0.9%
0.994 7038
0.8%
0.993 6163
0.7%
0.992 5277
0.6%
0.991 4884
0.5%
0.99 4144
0.5%
0.989 3810
0.4%
0.988 3573
0.4%
0.987 3272
0.4%

liveness
Real number (ℝ)

Distinct1796
Distinct (%)0.2%
Missing478
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.18662722
Minimum0
Maximum1
Zeros104
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-06-20T11:09:59.371084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0626
Q10.0962
median0.119
Q30.218
95-th percentile0.542
Maximum1
Range1
Interquartile range (IQR)0.1218

Descriptive statistics

Standard deviation0.16300863
Coefficient of variation (CV)0.87344505
Kurtosis6.8665257
Mean0.18662722
Median Absolute Deviation (MAD)0.0359
Skewness2.4855796
Sum167819.68
Variance0.026571812
MonotonicityNot monotonic
2024-06-20T11:09:59.502032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.111 14634
 
1.6%
0.11 13593
 
1.5%
0.109 12466
 
1.4%
0.108 12043
 
1.3%
0.107 11642
 
1.3%
0.112 11527
 
1.3%
0.106 10927
 
1.2%
0.105 10552
 
1.2%
0.104 10146
 
1.1%
0.103 9881
 
1.1%
Other values (1786) 781813
86.9%
ValueCountFrequency (%)
0 104
< 0.1%
0.00613 1
 
< 0.1%
0.00724 1
 
< 0.1%
0.00784 1
 
< 0.1%
0.00838 1
 
< 0.1%
0.00892 1
 
< 0.1%
0.0093 1
 
< 0.1%
0.00967 1
 
< 0.1%
0.01 1
 
< 0.1%
0.0104 1
 
< 0.1%
ValueCountFrequency (%)
1 7
 
< 0.1%
0.999 7
 
< 0.1%
0.998 7
 
< 0.1%
0.997 6
 
< 0.1%
0.996 11
 
< 0.1%
0.995 20
< 0.1%
0.994 24
< 0.1%
0.993 31
< 0.1%
0.992 33
< 0.1%
0.991 35
< 0.1%

trend
Categorical

MISSING 

Distinct4
Distinct (%)0.1%
Missing892662
Missing (%)99.2%
Memory size6.9 MiB
NEW_ENTRY
3224 
MOVE_DOWN
1972 
MOVE_UP
1517 
SAME_POSITION
327 

Length

Max length13
Median length9
Mean length8.7548295
Min length7

Characters and Unicode

Total characters61634
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNEW_ENTRY
2nd rowMOVE_DOWN
3rd rowNEW_ENTRY
4th rowNEW_ENTRY
5th rowNEW_ENTRY

Common Values

ValueCountFrequency (%)
NEW_ENTRY 3224
 
0.4%
MOVE_DOWN 1972
 
0.2%
MOVE_UP 1517
 
0.2%
SAME_POSITION 327
 
< 0.1%
(Missing) 892662
99.2%

Length

2024-06-20T11:09:59.625275image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-20T11:09:59.724178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
new_entry 3224
45.8%
move_down 1972
28.0%
move_up 1517
21.5%
same_position 327
 
4.6%

Most occurring characters

ValueCountFrequency (%)
E 10264
16.7%
N 8747
14.2%
_ 7040
11.4%
O 6115
9.9%
W 5196
8.4%
M 3816
 
6.2%
T 3551
 
5.8%
V 3489
 
5.7%
R 3224
 
5.2%
Y 3224
 
5.2%
Other values (6) 6968
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61634
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 10264
16.7%
N 8747
14.2%
_ 7040
11.4%
O 6115
9.9%
W 5196
8.4%
M 3816
 
6.2%
T 3551
 
5.8%
V 3489
 
5.7%
R 3224
 
5.2%
Y 3224
 
5.2%
Other values (6) 6968
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61634
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 10264
16.7%
N 8747
14.2%
_ 7040
11.4%
O 6115
9.9%
W 5196
8.4%
M 3816
 
6.2%
T 3551
 
5.8%
V 3489
 
5.7%
R 3224
 
5.2%
Y 3224
 
5.2%
Other values (6) 6968
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61634
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 10264
16.7%
N 8747
14.2%
_ 7040
11.4%
O 6115
9.9%
W 5196
8.4%
M 3816
 
6.2%
T 3551
 
5.8%
V 3489
 
5.7%
R 3224
 
5.2%
Y 3224
 
5.2%
Other values (6) 6968
11.3%

instrumentalness
Real number (ℝ)

ZEROS 

Distinct5402
Distinct (%)0.6%
Missing478
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.30045605
Minimum0
Maximum1
Zeros202479
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-06-20T11:09:59.848007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.27 × 10-6
median0.00535
Q30.787
95-th percentile0.939
Maximum1
Range1
Interquartile range (IQR)0.78699773

Descriptive statistics

Standard deviation0.39150411
Coefficient of variation (CV)1.3030329
Kurtosis-1.3384459
Mean0.30045605
Median Absolute Deviation (MAD)0.00535
Skewness0.71290578
Sum270177.29
Variance0.15327546
MonotonicityNot monotonic
2024-06-20T11:09:59.979907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 202479
 
22.5%
0.926 1903
 
0.2%
0.915 1889
 
0.2%
0.928 1884
 
0.2%
0.916 1881
 
0.2%
0.918 1878
 
0.2%
0.927 1875
 
0.2%
0.929 1859
 
0.2%
0.923 1848
 
0.2%
0.912 1845
 
0.2%
Other values (5392) 679883
75.6%
ValueCountFrequency (%)
0 202479
22.5%
1 × 10-6136
 
< 0.1%
1.01 × 10-6273
 
< 0.1%
1.02 × 10-6252
 
< 0.1%
1.03 × 10-6243
 
< 0.1%
1.04 × 10-6258
 
< 0.1%
1.05 × 10-6261
 
< 0.1%
1.06 × 10-6233
 
< 0.1%
1.07 × 10-6281
 
< 0.1%
1.08 × 10-6232
 
< 0.1%
ValueCountFrequency (%)
1 58
< 0.1%
0.999 58
< 0.1%
0.998 56
< 0.1%
0.997 51
< 0.1%
0.996 67
< 0.1%
0.995 87
< 0.1%
0.994 79
< 0.1%
0.993 101
< 0.1%
0.992 88
< 0.1%
0.991 107
< 0.1%

loudness
Real number (ℝ)

Distinct39362
Distinct (%)4.4%
Missing478
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean-10.833539
Minimum-60
Maximum5.096
Zeros2
Zeros (%)< 0.1%
Negative898082
Negative (%)99.8%
Memory size6.9 MiB
2024-06-20T11:10:00.104370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-60
5-th percentile-25.604
Q1-13.643
median-8.74
Q3-5.983
95-th percentile-3.468
Maximum5.096
Range65.096
Interquartile range (IQR)7.66

Descriptive statistics

Standard deviation6.9411068
Coefficient of variation (CV)-0.64070539
Kurtosis2.1901024
Mean-10.833539
Median Absolute Deviation (MAD)3.355
Skewness-1.4521399
Sum-9741778.3
Variance48.178964
MonotonicityNot monotonic
2024-06-20T11:10:00.236526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.862 127
 
< 0.1%
-6.217 124
 
< 0.1%
-7.078 122
 
< 0.1%
-5.743 121
 
< 0.1%
-5.623 120
 
< 0.1%
-5.756 119
 
< 0.1%
-5.582 118
 
< 0.1%
-5.696 117
 
< 0.1%
-5.772 116
 
< 0.1%
-6.017 116
 
< 0.1%
Other values (39352) 898024
99.8%
(Missing) 478
 
0.1%
ValueCountFrequency (%)
-60 21
< 0.1%
-58.713 1
 
< 0.1%
-56.059 1
 
< 0.1%
-54.077 1
 
< 0.1%
-53.986 1
 
< 0.1%
-53.985 1
 
< 0.1%
-53.714 1
 
< 0.1%
-53.461 1
 
< 0.1%
-53.025 1
 
< 0.1%
-52.863 1
 
< 0.1%
ValueCountFrequency (%)
5.096 1
< 0.1%
4.638 1
< 0.1%
4.142 1
< 0.1%
4.14 1
< 0.1%
3.97 1
< 0.1%
3.833 1
< 0.1%
3.793 1
< 0.1%
3.744 1
< 0.1%
3.554 1
< 0.1%
3.537 1
< 0.1%

name
Text

Distinct578959
Distinct (%)64.4%
Missing487
Missing (%)0.1%
Memory size6.9 MiB
2024-06-20T11:10:00.600681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length292
Median length235
Mean length19.724947
Min length1

Characters and Unicode

Total characters17736968
Distinct characters4194
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique478009 ?
Unique (%)53.2%

Sample

1st rowFriday I’m In Love - Recorded at Spotify Studios NYC
2nd rowI Love You Always Forever
3rd rowLove Too Deep - Radio Edit
4th rowNo Tiren Las Botellas
5th rowEl Momento de Despertar - Blue Sky Mix
ValueCountFrequency (%)
145204
 
4.4%
the 98945
 
3.0%
in 46198
 
1.4%
of 36979
 
1.1%
you 36639
 
1.1%
a 34857
 
1.1%
i 33541
 
1.0%
me 28296
 
0.9%
feat 26043
 
0.8%
no 25724
 
0.8%
Other values (190024) 2787284
84.5%
2024-06-20T11:10:01.116870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2400444
 
13.5%
e 1542615
 
8.7%
a 1069910
 
6.0%
o 1060629
 
6.0%
i 922456
 
5.2%
n 893442
 
5.0%
r 809684
 
4.6%
t 759120
 
4.3%
s 580249
 
3.3%
l 572173
 
3.2%
Other values (4184) 7126246
40.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17736968
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2400444
 
13.5%
e 1542615
 
8.7%
a 1069910
 
6.0%
o 1060629
 
6.0%
i 922456
 
5.2%
n 893442
 
5.0%
r 809684
 
4.6%
t 759120
 
4.3%
s 580249
 
3.3%
l 572173
 
3.2%
Other values (4184) 7126246
40.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17736968
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2400444
 
13.5%
e 1542615
 
8.7%
a 1069910
 
6.0%
o 1060629
 
6.0%
i 922456
 
5.2%
n 893442
 
5.0%
r 809684
 
4.6%
t 759120
 
4.3%
s 580249
 
3.3%
l 572173
 
3.2%
Other values (4184) 7126246
40.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17736968
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2400444
 
13.5%
e 1542615
 
8.7%
a 1069910
 
6.0%
o 1060629
 
6.0%
i 922456
 
5.2%
n 893442
 
5.0%
r 809684
 
4.6%
t 759120
 
4.3%
s 580249
 
3.3%
l 572173
 
3.2%
Other values (4184) 7126246
40.2%

Interactions

2024-06-20T11:09:35.065256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:08:52.513077image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:08:54.260945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:08:56.928168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:08:59.576719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:02.311473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:04.992438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:07.885400image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:10.546801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:13.345943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:15.009248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:16.776614image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:18.637395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:21.313787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:24.016876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:26.850852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:29.554951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:32.378152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:35.235838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:08:52.627393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:08:54.428178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:08:57.099374image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:08:59.752430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:02.487743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:05.160218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:08.058381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:10.716000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:13.449286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:15.110681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:16.879783image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:18.803894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:21.490495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:24.186082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:27.021852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:29.725642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:32.549473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:35.403138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:08:52.724993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:08:54.592299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:08:57.261865image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:08:59.920254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:02.652316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:05.322333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:08.221571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:10.880704image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:13.540598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:15.208047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:16.976995image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:18.970838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:21.658685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:24.490715image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:27.189735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:29.892011image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-06-20T11:08:56.346042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:08:58.998407image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:01.719288image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:04.406220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:07.300910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:09.967368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:12.629045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:14.638644image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:16.368723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:18.244794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:20.737567image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:23.424921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:26.261483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:28.961642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:31.659159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:34.487960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:37.372735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:08:53.975252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:08:56.508886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:08:59.160245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:01.884929image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:04.571405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:07.464934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:10.128985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:12.922632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:14.728641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:16.472396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:18.339564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:20.896949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:23.594854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:26.426298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:29.126327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:31.953068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:34.650962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:37.536958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:08:54.068761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:08:56.671321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:08:59.322381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:02.050949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:04.733985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:07.625542image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:10.291008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:13.086244image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:14.817067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:16.574815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:18.434290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:21.060687image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:23.754233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:26.592843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:29.293931image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:32.117750image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:34.809667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:37.705638image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:08:54.162200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:08:56.834096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:08:59.484160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:02.219205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:04.900755image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:07.791939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:10.454869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:13.251563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:14.908804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:16.676679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:18.529178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:21.221761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:23.921274image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:26.758730image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:29.460709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:32.285916image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-20T11:09:34.971443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-06-20T11:09:38.282695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-20T11:09:39.894904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-20T11:09:45.146903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

track_idstreamsartist_followersgenresalbum_total_trackstrack_artistsartist_popularityexplicittempochartalbum_release_dateenergykeyadded_atpopularitytrack_album_albumduration_msavailable_marketstrack_track_numberrankmodetime_signaturealbum_namespeechinessregiondanceabilityvalenceacousticnesslivenesstrendinstrumentalnessloudnessname
007vS8obfeZbr8H4MgQfXR7NaN2338837.0['indie pop', 'la indie', 'pov: indie']2.0Phoebe Bridgers74.0False97.129NaN2018-12-050.1237.02021-07-04T11:06:43Z0.0singleNaN[]2.0NaN1.04.0Spotify Singles0.0407NaN0.3730.1380.94800.0816NaN0.000000-15.193Friday I’m In Love - Recorded at Spotify Studios NYC
11PEqh7awkpuepLBSq8ZwqD27156.084914.0['lilith', 'new wave pop']11.0NaN51.0False103.773top2001996-04-160.4535.02023-01-25T01:09:09Z71.0NaN239960.0['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']NaN187.01.04.0Now in a Minute0.0348France0.7440.1220.62700.0898NEW_ENTRY0.421000-11.977I Love You Always Forever
27E8pPgBY84oDaXRcqODavRNaN59150.0['deep groove house', 'house', 'tech house']2.0NaN54.0False122.030NaN2014-07-070.8789.0NaN0.0NaNNaN[]NaNNaN0.04.0Love Too Deep0.0357NaN0.7470.8970.07940.3700NaN0.000531-5.209Love Too Deep - Radio Edit
30Atml4huw4Fgyk6YSHiK4MNaN1528.0[]15.0NaN0.0False84.099NaN2001-01-240.4847.0NaN0.0NaNNaN[]NaNNaN1.04.0Voces Del Pueblo0.0356NaN0.6040.5640.10000.0865NaN0.000000-7.097No Tiren Las Botellas
44WYDmIZrwxBHdBYdvi5oQONaN6776.0['chill lounge', 'deep chill']26.0NaN28.0False156.017NaN2014-10-030.4470.0NaN7.0NaNNaN['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'PR', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']NaNNaN1.04.0The Smooth Operator - Cosmopolitan Lounge Music0.0613NaN0.7610.7610.06160.0822NaN0.873000-10.961El Momento de Despertar - Blue Sky Mix
50awG4a7t5UrmZZ4PZVNav3NaN28431.0['post-disco']29.0Oliver Cheatham50.0False116.545NaN2020-05-080.61111.02021-12-19T05:12:52Z0.0compilationNaN[]22.0NaN0.04.0Disco Essentials0.0573NaN0.8650.9630.22600.0572NaN0.082000-11.571Get Down Saturday Night
61DihsSBztAD6qJ5TTEE90FNaN355425.0['classic italian pop', 'italian adult pop']11.0NaN44.0False120.012NaN2015-02-120.63311.0NaN0.0NaNNaN[]NaNNaN0.04.0Naif0.0323NaN0.7130.5180.07850.1060NaN0.000003-7.425Senza fare sul serio
72uwnP6tZVVmTovzX5ELooyNaN23324247.0['conscious hip hop', 'hip hop', 'north carolina hip hop', 'rap']21.0NaN84.0True99.992NaN2013-06-180.6081.02023-10-03T23:26:09Z80.0NaNNaN['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']NaNNaN1.04.0Born Sinner (Deluxe Version)0.2160NaN0.6670.4750.32400.4260NaN0.000198-7.054Power Trip (feat. Miguel)
805HYPQPzZXyvirt2GsbvutNaN2166585.0['classic country pop', 'classic texas country', 'country', 'country rock', 'nashville sound', 'outlaw country', 'singer-songwriter']11.0Willie Nelson69.0False80.972NaN2016-02-260.2172.0NaN18.0albumNaN['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'PR', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']9.0NaN1.04.0Summertime: Willie Nelson Sings Gershwin0.0326NaN0.5530.1970.84200.1010NaN0.000066-12.973Embraceable You (feat. Sheryl Crow)
93FUPP0Q5E2JKopEUHXIwdwNaN195.0[]3.0NaN31.0False89.206NaN2023-01-080.1562.0NaN35.0NaNNaN['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'PR', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']NaNNaN0.04.0Somewhere Only We Go0.0320NaN0.4420.1820.98700.1860NaN0.900000-17.749Somewhere Only We Go
track_idstreamsartist_followersgenresalbum_total_trackstrack_artistsartist_popularityexplicittempochartalbum_release_dateenergykeyadded_atpopularitytrack_album_albumduration_msavailable_marketstrack_track_numberrankmodetime_signaturealbum_namespeechinessregiondanceabilityvalenceacousticnesslivenesstrendinstrumentalnessloudnessname
8996920DXGme9sJXTlwkvb946bwJNaN10936.0['tech house']1.0NaN31.0False130.010NaN2023-10-130.95301.0NaN14.0NaNNaN['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'PR', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']NaNNaN0.04.0Hallucination (George Z Remix)0.0540NaN0.6150.3660.0171000.2840NaN0.54400-5.755Hallucination - George Z Remix
89969304OoQIbg82ONtiqiQE38v0NaN2211.0['classic city pop']7.0NaN16.0False107.781NaN1978-12-210.18104.0NaN24.0NaNNaN['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'PR', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']NaNNaN0.04.0バッド・アニマ0.0345NaN0.6110.3410.9420000.1090NaN0.06410-19.894スペイス・トラヴェラー
8996941FvwBwSHfx61urFd7Eco2ENaN29.0[]1.0Davi Barufaldi2.0False110.041NaN2021-07-170.32502.0NaN15.0singleNaN['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'PR', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']1.0NaN1.04.0Santo Espirito0.0368NaN0.7670.2680.4490000.0903NaN0.89900-10.741Santo Espirito
8996955EuHXAbiKSPvjE3Towh9g3NaN37474.0['deep house', 'float house']1.0NaN33.0False128.011NaN2022-04-080.70902.0NaN12.0NaNNaN['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'PR', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']NaNNaN1.04.0Proto/Emotions0.0528NaN0.8070.7400.0023200.0445NaN0.78600-11.361Proto/Emotions
8996964Bmp7mEwtvF7xeUdKwXtPyNaN478.0[]8.0NaN0.0False120.064NaN2023-04-010.89209.0NaN0.0NaNNaN['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'PR', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']NaNNaN1.04.0We're Installing Three New Elevators0.1320NaN0.5480.2850.0007020.1220NaN0.00664-11.452Chainsaw
8996977vHTQ2jmlgJrJLG4JuXIKeNaN40725.0['melodic techno']1.0NaN36.0False123.006NaN2022-02-250.743011.0NaN28.0NaNNaN['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'PR', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']NaNNaN1.04.0Rock It0.0532NaN0.8030.1760.0012000.0618NaN0.30800-6.241Rock It
8996986V8OtQwEXvXikl6hgzMadTNaN10523.0['zouk riddim']25.0NaN32.0False92.999NaN20160.75601.0NaN0.0NaNNaN['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'PR', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']NaNNaN1.04.0Zouk Love Session (Valentine's Day Edition)0.0376NaN0.6550.4010.0099200.3370NaN0.02800-7.407Pour toi
8996994ytUwCIZoGFwwkRgnjmBLuNaN43895.0['chanson', 'classic french pop', 'french pop']9.0NaN42.0False85.571NaN2017-02-030.63603.0NaN0.0NaNNaN[]NaNNaN1.04.0Calicoba (2017 Remastered)0.0343NaN0.5960.4090.0558000.0629NaN0.00000-8.882Ville De Lumière - 2017 Remastered
8997003ZlnnDeAYpRznBFubDVdfHNaN3.0[]30.0NaN6.0False61.653NaN2023-07-210.09083.0NaN0.0NaNNaN['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'PR', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']NaNNaN1.03.0A Harp Journey, vol. I0.0897NaN0.6180.8500.9290000.0922NaN0.89800-25.645Dancing with the Wind (Harp Version)
8997012NC6rTKrPhxakFzGMdTsxONaN5201016.0['classical', 'classical era', 'early romantic era', 'german romanticism']8.0NaN68.0False150.068NaN2008-02-210.18305.0NaN1.0NaNNaN['AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'DE', 'EC', 'EE', 'SV', 'FI', 'FR', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'IE', 'IT', 'LV', 'LT', 'LU', 'MY', 'MT', 'MX', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'SK', 'ES', 'SE', 'CH', 'TW', 'TR', 'UY', 'US', 'GB', 'AD', 'LI', 'MC', 'ID', 'JP', 'TH', 'VN', 'RO', 'IL', 'ZA', 'SA', 'AE', 'BH', 'QA', 'OM', 'KW', 'EG', 'MA', 'DZ', 'TN', 'LB', 'JO', 'PS', 'IN', 'BY', 'KZ', 'MD', 'UA', 'AL', 'BA', 'HR', 'ME', 'MK', 'RS', 'SI', 'KR', 'BD', 'PK', 'LK', 'GH', 'KE', 'NG', 'TZ', 'UG', 'AG', 'AM', 'BS', 'BB', 'BZ', 'BT', 'BW', 'BF', 'CV', 'CW', 'DM', 'FJ', 'GM', 'GE', 'GD', 'GW', 'GY', 'HT', 'JM', 'KI', 'LS', 'LR', 'MW', 'MV', 'ML', 'MH', 'FM', 'NA', 'NR', 'NE', 'PW', 'PG', 'PR', 'WS', 'SM', 'ST', 'SN', 'SC', 'SL', 'SB', 'KN', 'LC', 'VC', 'SR', 'TL', 'TO', 'TT', 'TV', 'VU', 'AZ', 'BN', 'BI', 'KH', 'CM', 'TD', 'KM', 'GQ', 'SZ', 'GA', 'GN', 'KG', 'LA', 'MO', 'MR', 'MN', 'NP', 'RW', 'TG', 'UZ', 'ZW', 'BJ', 'MG', 'MU', 'MZ', 'AO', 'CI', 'DJ', 'ZM', 'CD', 'CG', 'IQ', 'LY', 'TJ', 'VE', 'ET', 'XK']NaNNaN1.04.0Beethoven: Symphonies Nos. 5 and 80.0378NaN0.4030.2740.9450000.1000NaN0.68600-19.031Symphony No. 8 in F Major, Op. 93: IV. Allegro molto